Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations16512
Missing cells158
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory113.0 B

Variable types

Numeric8
Categorical2

Alerts

households is highly overall correlated with population and 2 other fieldsHigh correlation
income_cat is highly overall correlated with median_incomeHigh correlation
latitude is highly overall correlated with longitudeHigh correlation
longitude is highly overall correlated with latitudeHigh correlation
median_income is highly overall correlated with income_catHigh correlation
population is highly overall correlated with households and 2 other fieldsHigh correlation
total_bedrooms is highly overall correlated with households and 2 other fieldsHigh correlation
total_rooms is highly overall correlated with households and 2 other fieldsHigh correlation

Reproduction

Analysis started2025-10-10 10:37:40.020019
Analysis finished2025-10-10 10:37:46.743885
Duration6.72 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

longitude
Real number (ℝ)

High correlation 

Distinct825
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-119.57564
Minimum-124.35
Maximum-114.31
Zeros0
Zeros (%)0.0%
Negative16512
Negative (%)100.0%
Memory size774.0 KiB
2025-10-10T16:37:47.096974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-124.35
5-th percentile-122.47
Q1-121.8
median-118.51
Q3-118.01
95-th percentile-117.07
Maximum-114.31
Range10.04
Interquartile range (IQR)3.79

Descriptive statistics

Standard deviation2.0018281
Coefficient of variation (CV)-0.016741103
Kurtosis-1.3347154
Mean-119.57564
Median Absolute Deviation (MAD)1.29
Skewness-0.29355541
Sum-1974432.9
Variance4.0073156
MonotonicityNot monotonic
2025-10-10T16:37:47.214715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.31133
 
0.8%
-118.3129
 
0.8%
-118.29121
 
0.7%
-118.35113
 
0.7%
-118.27113
 
0.7%
-118.28107
 
0.6%
-118.19107
 
0.6%
-118.36101
 
0.6%
-118.14101
 
0.6%
-118.2699
 
0.6%
Other values (815)15388
93.2%
ValueCountFrequency (%)
-124.351
 
< 0.1%
-124.32
 
< 0.1%
-124.271
 
< 0.1%
-124.261
 
< 0.1%
-124.251
 
< 0.1%
-124.233
< 0.1%
-124.221
 
< 0.1%
-124.213
< 0.1%
-124.194
< 0.1%
-124.185
< 0.1%
ValueCountFrequency (%)
-114.311
< 0.1%
-114.471
< 0.1%
-114.491
< 0.1%
-114.551
< 0.1%
-114.572
< 0.1%
-114.582
< 0.1%
-114.592
< 0.1%
-114.61
< 0.1%
-114.631
< 0.1%
-114.641
< 0.1%

latitude
Real number (ℝ)

High correlation 

Distinct839
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.639314
Minimum32.54
Maximum41.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:47.330814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum32.54
5-th percentile32.82
Q133.94
median34.26
Q337.72
95-th percentile38.95
Maximum41.95
Range9.41
Interquartile range (IQR)3.78

Descriptive statistics

Standard deviation2.1379628
Coefficient of variation (CV)0.059988887
Kurtosis-1.1172537
Mean35.639314
Median Absolute Deviation (MAD)1.23
Skewness0.46145588
Sum588476.36
Variance4.570885
MonotonicityNot monotonic
2025-10-10T16:37:47.438714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.06207
 
1.3%
34.05193
 
1.2%
34.07191
 
1.2%
34.08191
 
1.2%
34.09169
 
1.0%
34.02169
 
1.0%
34.04164
 
1.0%
34.1158
 
1.0%
34.03152
 
0.9%
33.97147
 
0.9%
Other values (829)14771
89.5%
ValueCountFrequency (%)
32.541
 
< 0.1%
32.552
 
< 0.1%
32.568
 
< 0.1%
32.5715
0.1%
32.5820
0.1%
32.5910
0.1%
32.69
0.1%
32.6114
0.1%
32.6212
0.1%
32.6315
0.1%
ValueCountFrequency (%)
41.951
 
< 0.1%
41.921
 
< 0.1%
41.881
 
< 0.1%
41.863
< 0.1%
41.841
 
< 0.1%
41.811
 
< 0.1%
41.83
< 0.1%
41.791
 
< 0.1%
41.782
< 0.1%
41.771
 
< 0.1%

housing_median_age
Real number (ℝ)

Distinct52
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.653404
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:47.554742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q118
median29
Q337
95-th percentile52
Maximum52
Range51
Interquartile range (IQR)19

Descriptive statistics

Standard deviation12.574819
Coefficient of variation (CV)0.43885951
Kurtosis-0.79678594
Mean28.653404
Median Absolute Deviation (MAD)10
Skewness0.059468121
Sum473125
Variance158.12606
MonotonicityNot monotonic
2025-10-10T16:37:47.662689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
521027
 
6.2%
36696
 
4.2%
35668
 
4.0%
16626
 
3.8%
17562
 
3.4%
34557
 
3.4%
33504
 
3.1%
26491
 
3.0%
25458
 
2.8%
32449
 
2.7%
Other values (42)10474
63.4%
ValueCountFrequency (%)
13
 
< 0.1%
246
 
0.3%
351
 
0.3%
4150
0.9%
5188
1.1%
6131
0.8%
7139
0.8%
8166
1.0%
9163
1.0%
10212
1.3%
ValueCountFrequency (%)
521027
6.2%
5139
 
0.2%
50106
 
0.6%
49108
 
0.7%
48146
 
0.9%
47152
 
0.9%
46180
 
1.1%
45238
 
1.4%
44268
 
1.6%
43289
 
1.8%

total_rooms
Real number (ℝ)

High correlation 

Distinct5494
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2622.5398
Minimum6
Maximum39320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:47.773431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile624.55
Q11443
median2119
Q33141
95-th percentile6187.8
Maximum39320
Range39314
Interquartile range (IQR)1698

Descriptive statistics

Standard deviation2138.4171
Coefficient of variation (CV)0.81539929
Kurtosis31.677031
Mean2622.5398
Median Absolute Deviation (MAD)799
Skewness4.0008355
Sum43303377
Variance4572827.6
MonotonicityNot monotonic
2025-10-10T16:37:47.891707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152716
 
0.1%
158215
 
0.1%
147114
 
0.1%
170513
 
0.1%
146213
 
0.1%
161313
 
0.1%
174512
 
0.1%
251212
 
0.1%
205312
 
0.1%
170112
 
0.1%
Other values (5484)16380
99.2%
ValueCountFrequency (%)
61
< 0.1%
152
< 0.1%
161
< 0.1%
182
< 0.1%
192
< 0.1%
201
< 0.1%
211
< 0.1%
221
< 0.1%
241
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
393201
< 0.1%
379371
< 0.1%
320541
< 0.1%
304501
< 0.1%
304011
< 0.1%
282581
< 0.1%
277001
< 0.1%
251351
< 0.1%
239151
< 0.1%
238661
< 0.1%

total_bedrooms
Real number (ℝ)

High correlation 

Distinct1810
Distinct (%)11.1%
Missing158
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean534.91464
Minimum2
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:47.999992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile137
Q1295
median433
Q3644
95-th percentile1268
Maximum6210
Range6208
Interquartile range (IQR)349

Descriptive statistics

Standard deviation412.66565
Coefficient of variation (CV)0.77146075
Kurtosis19.551919
Mean534.91464
Median Absolute Deviation (MAD)162
Skewness3.2692705
Sum8747994
Variance170292.94
MonotonicityNot monotonic
2025-10-10T16:37:48.106445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27244
 
0.3%
28044
 
0.3%
39343
 
0.3%
34541
 
0.2%
33141
 
0.2%
32841
 
0.2%
30940
 
0.2%
39439
 
0.2%
34839
 
0.2%
34039
 
0.2%
Other values (1800)15943
96.6%
(Missing)158
 
1.0%
ValueCountFrequency (%)
21
 
< 0.1%
33
< 0.1%
44
< 0.1%
55
< 0.1%
63
< 0.1%
75
< 0.1%
87
< 0.1%
95
< 0.1%
106
< 0.1%
116
< 0.1%
ValueCountFrequency (%)
62101
< 0.1%
54711
< 0.1%
52901
< 0.1%
50331
< 0.1%
49571
< 0.1%
48191
< 0.1%
45851
< 0.1%
44571
< 0.1%
44071
< 0.1%
43861
< 0.1%

population
Real number (ℝ)

High correlation 

Distinct3619
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1419.6874
Minimum3
Maximum35682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:48.207602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile347.55
Q1784
median1164
Q31719
95-th percentile3276.9
Maximum35682
Range35679
Interquartile range (IQR)935

Descriptive statistics

Standard deviation1115.663
Coefficient of variation (CV)0.7858512
Kurtosis71.792077
Mean1419.6874
Median Absolute Deviation (MAD)441
Skewness4.7415683
Sum23441878
Variance1244704
MonotonicityNot monotonic
2025-10-10T16:37:48.319628image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122721
 
0.1%
75320
 
0.1%
100520
 
0.1%
76119
 
0.1%
89119
 
0.1%
82519
 
0.1%
66218
 
0.1%
79318
 
0.1%
128318
 
0.1%
98618
 
0.1%
Other values (3609)16322
98.8%
ValueCountFrequency (%)
31
 
< 0.1%
83
< 0.1%
92
< 0.1%
111
 
< 0.1%
132
< 0.1%
143
< 0.1%
152
< 0.1%
172
< 0.1%
181
 
< 0.1%
191
 
< 0.1%
ValueCountFrequency (%)
356821
< 0.1%
163051
< 0.1%
161221
< 0.1%
155071
< 0.1%
150371
< 0.1%
132511
< 0.1%
124271
< 0.1%
122031
< 0.1%
119731
< 0.1%
112721
< 0.1%

households
Real number (ℝ)

High correlation 

Distinct1691
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean497.01181
Minimum2
Maximum5358
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:48.429454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile124
Q1279
median408
Q3602
95-th percentile1150
Maximum5358
Range5356
Interquartile range (IQR)323

Descriptive statistics

Standard deviation375.69616
Coefficient of variation (CV)0.75590992
Kurtosis19.294653
Mean497.01181
Median Absolute Deviation (MAD)150
Skewness3.2220813
Sum8206659
Variance141147.6
MonotonicityNot monotonic
2025-10-10T16:37:48.547001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42947
 
0.3%
33547
 
0.3%
38645
 
0.3%
30645
 
0.3%
27844
 
0.3%
31644
 
0.3%
37543
 
0.3%
28442
 
0.3%
29742
 
0.3%
34042
 
0.3%
Other values (1681)16071
97.3%
ValueCountFrequency (%)
21
 
< 0.1%
33
 
< 0.1%
43
 
< 0.1%
55
< 0.1%
64
< 0.1%
76
< 0.1%
87
< 0.1%
98
< 0.1%
107
< 0.1%
113
 
< 0.1%
ValueCountFrequency (%)
53581
< 0.1%
51891
< 0.1%
50501
< 0.1%
47691
< 0.1%
43391
< 0.1%
42041
< 0.1%
41761
< 0.1%
40721
< 0.1%
40121
< 0.1%
39581
< 0.1%

median_income
Real number (ℝ)

High correlation 

Distinct10905
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8758843
Minimum0.4999
Maximum15.0001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size774.0 KiB
2025-10-10T16:37:48.654694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.4999
5-th percentile1.603355
Q12.56695
median3.54155
Q34.745325
95-th percentile7.308945
Maximum15.0001
Range14.5002
Interquartile range (IQR)2.178375

Descriptive statistics

Standard deviation1.9049305
Coefficient of variation (CV)0.49148282
Kurtosis4.9332556
Mean3.8758843
Median Absolute Deviation (MAD)1.06035
Skewness1.6533529
Sum63998.601
Variance3.6287603
MonotonicityNot monotonic
2025-10-10T16:37:48.764074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.87538
 
0.2%
15.000138
 
0.2%
2.62537
 
0.2%
3.12537
 
0.2%
3.37533
 
0.2%
3.62532
 
0.2%
4.12532
 
0.2%
3.87531
 
0.2%
329
 
0.2%
428
 
0.2%
Other values (10895)16177
98.0%
ValueCountFrequency (%)
0.49999
0.1%
0.5367
< 0.1%
0.54951
 
< 0.1%
0.64331
 
< 0.1%
0.67751
 
< 0.1%
0.68251
 
< 0.1%
0.69911
 
< 0.1%
0.70071
 
< 0.1%
0.70251
 
< 0.1%
0.70541
 
< 0.1%
ValueCountFrequency (%)
15.000138
0.2%
152
 
< 0.1%
14.58331
 
< 0.1%
14.42191
 
< 0.1%
14.41131
 
< 0.1%
14.29591
 
< 0.1%
13.9471
 
< 0.1%
13.85561
 
< 0.1%
13.80931
 
< 0.1%
13.68421
 
< 0.1%

ocean_proximity
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size774.0 KiB
<1H OCEAN
7277 
INLAND
5262 
NEAR OCEAN
2124 
NEAR BAY
1847 
ISLAND
 
2

Length

Max length10
Median length9
Mean length8.0603803
Min length6

Characters and Unicode

Total characters133093
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowINLAND
2nd rowNEAR OCEAN
3rd rowINLAND
4th rowNEAR OCEAN
5th row<1H OCEAN

Common Values

ValueCountFrequency (%)
<1H OCEAN7277
44.1%
INLAND5262
31.9%
NEAR OCEAN2124
 
12.9%
NEAR BAY1847
 
11.2%
ISLAND2
 
< 0.1%

Length

2025-10-10T16:37:48.876971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T16:37:48.948657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ocean9401
33.9%
1h7277
26.2%
inland5262
19.0%
near3971
14.3%
bay1847
 
6.7%
island2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N23898
18.0%
A20483
15.4%
E13372
10.0%
11248
8.5%
O9401
 
7.1%
C9401
 
7.1%
<7277
 
5.5%
H7277
 
5.5%
17277
 
5.5%
I5264
 
4.0%
Other values (6)18195
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)133093
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N23898
18.0%
A20483
15.4%
E13372
10.0%
11248
8.5%
O9401
 
7.1%
C9401
 
7.1%
<7277
 
5.5%
H7277
 
5.5%
17277
 
5.5%
I5264
 
4.0%
Other values (6)18195
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)133093
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N23898
18.0%
A20483
15.4%
E13372
10.0%
11248
8.5%
O9401
 
7.1%
C9401
 
7.1%
<7277
 
5.5%
H7277
 
5.5%
17277
 
5.5%
I5264
 
4.0%
Other values (6)18195
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)133093
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N23898
18.0%
A20483
15.4%
E13372
10.0%
11248
8.5%
O9401
 
7.1%
C9401
 
7.1%
<7277
 
5.5%
H7277
 
5.5%
17277
 
5.5%
I5264
 
4.0%
Other values (6)18195
13.7%

income_cat
Categorical

High correlation 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size661.4 KiB
3
5789 
2
5265 
4
2911 
5
1890 
1
657 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters16512
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row5
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
35789
35.1%
25265
31.9%
42911
17.6%
51890
 
11.4%
1657
 
4.0%

Length

2025-10-10T16:37:49.039488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-10T16:37:49.395502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
35789
35.1%
25265
31.9%
42911
17.6%
51890
 
11.4%
1657
 
4.0%

Most occurring characters

ValueCountFrequency (%)
35789
35.1%
25265
31.9%
42911
17.6%
51890
 
11.4%
1657
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)16512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35789
35.1%
25265
31.9%
42911
17.6%
51890
 
11.4%
1657
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35789
35.1%
25265
31.9%
42911
17.6%
51890
 
11.4%
1657
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35789
35.1%
25265
31.9%
42911
17.6%
51890
 
11.4%
1657
 
4.0%

Interactions

2025-10-10T16:37:45.784535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.435963image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.147024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.806885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.519996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.558422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.307474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.063470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.875936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.517399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.226164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.896227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.615309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.646414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.403171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.160008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.970651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.608089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.303026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.975320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.708666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.735018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.492399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.247934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:46.056042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.699469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.386977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.061607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.800378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.840410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.585903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.340576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:46.152632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.790867image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.475414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.155369image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.181830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.945051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.678447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.438248image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:46.242707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.877809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.556193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.258782image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.274293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.031766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.772273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.524057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:46.344871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:40.965331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.641752image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.348333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.368582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.121695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.866720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.610502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:46.435375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.060407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:41.725819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:42.431971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:43.466070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.208035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:44.958306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-10T16:37:45.689213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-10T16:37:49.470851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
householdshousing_median_ageincome_catlatitudelongitudemedian_incomeocean_proximitypopulationtotal_bedroomstotal_rooms
households1.000-0.2850.035-0.0760.0630.0270.0300.9040.9750.905
housing_median_age-0.2851.0000.0970.026-0.147-0.1410.192-0.286-0.311-0.360
income_cat0.0350.0971.0000.1210.1160.7350.1210.0220.0380.090
latitude-0.0760.0260.1211.000-0.880-0.0810.470-0.126-0.058-0.017
longitude0.063-0.1470.116-0.8801.000-0.0150.4240.1260.0670.041
median_income0.027-0.1410.735-0.081-0.0151.0000.1230.002-0.0090.270
ocean_proximity0.0300.1920.1210.4700.4240.1231.0000.0160.0220.022
population0.904-0.2860.022-0.1260.1260.0020.0161.0000.8700.815
total_bedrooms0.975-0.3110.038-0.0580.067-0.0090.0220.8701.0000.915
total_rooms0.905-0.3600.090-0.0170.0410.2700.0220.8150.9151.000

Missing values

2025-10-10T16:37:46.564908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-10T16:37:46.669838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximityincome_cat
12655-121.4638.5229.03873.0797.02237.0706.02.1736INLAND2
15502-117.2333.097.05320.0855.02015.0768.06.3373NEAR OCEAN5
2908-119.0435.3744.01618.0310.0667.0300.02.8750INLAND2
14053-117.1332.7524.01877.0519.0898.0483.02.2264NEAR OCEAN2
20496-118.7034.2827.03536.0646.01837.0580.04.4964<1H OCEAN3
1481-122.0437.9628.01207.0252.0724.0252.03.6964NEAR BAY3
18125-122.0337.3323.04221.0671.01782.0641.07.4863<1H OCEAN5
5830-118.3134.2036.01692.0263.0778.0278.05.0865<1H OCEAN4
17989-121.9537.2717.01330.0271.0408.0258.01.7171<1H OCEAN2
4861-118.2834.0229.0515.0229.02690.0217.00.4999<1H OCEAN1
longitudelatitudehousing_median_agetotal_roomstotal_bedroomspopulationhouseholdsmedian_incomeocean_proximityincome_cat
12396-116.2933.6712.05048.0842.0883.0391.05.6918INLAND4
16476-121.2738.1335.02607.0685.02016.0618.01.7500INLAND2
2271-119.8036.7843.02382.0431.0874.0380.03.5542INLAND3
6980-118.0133.9736.01451.0224.0608.0246.06.0648<1H OCEAN5
5206-118.2833.9341.0936.0257.0913.0226.02.0313<1H OCEAN2
15174-117.0733.0314.06665.01231.02026.01001.05.0900<1H OCEAN4
12661-121.4238.5115.07901.01422.04769.01418.02.8139INLAND2
19263-122.7238.4448.0707.0166.0458.0172.03.1797<1H OCEAN3
19140-122.7038.3114.03155.0580.01208.0501.04.1964<1H OCEAN3
19773-122.1439.9727.01079.0222.0625.0197.03.1319INLAND3